Senior Research Engineer
Stephen Jiggins works at the intersection of machine learning research and software engineering. During his PhD and postdoctoral research in high energy physics at CERN, he built production-grade, petabyte-scale data-processing pipelines and developed machine learning methods to accelerate simulation workflows. Most recently, he was a Helmholtz AI–funded Principal Investigator at DESY, leading research in probabilistic machine learning and generative models for data-intensive scientific fields. His research interests include statistics, probabilistic machine learning, and generative AI methods, with a focus on reliable generative systems.
Group Involvement, NEC Laboratories Europe
Reliable GenAI Solutions
Research Interests
- Generative AI
- Probabilistic Theory
- Statistics
- Probabilistic Machine Learning
Publications
- Matthew Drnevich, Stephen Jiggins, Kyle Cranmer, Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function, arXiv preprint, arXiv:2512.19913, 2025.
- Matthew Drnevich, Stephen Jiggins, Judith Katzy, Kyle Cranmer, Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data, Machine Learning: Science and Technology, 6(4), 045023, 2025.